Deploying deep neural networks for risk-sensitive tasks necessitates an
uncertainty estimation mechanism. This paper introduces hierarchical selective
classification, extending selective classification to a hierarchical setting.
Our approach leverages the inherent structure of class relationships, enabling
models to reduce the specificity of their predictions when faced with
uncertainty. In this paper, we first formalize hierarchical risk and coverage,
and introduce hierarchical risk-coverage curves. Next, we develop algorithms
for hierarchical selective classification (which we refer to as "inference
rules"), and propose an efficient algorithm that guarantees a target accuracy
constraint with high probability. Lastly, we conduct extensive empirical
studies on over a thousand ImageNet classifiers, revealing that training
regimes such as CLIP, pretraining on ImageNet21k and knowledge distillation
boost hierarchical selective performance.

部署深度神经网络用于风险敏感任务需要一个不确定性估计机制。本文介绍了分层选择分类，将选择分类扩展到分层环境中。我们的方法利用类别关系的固有结构，使模型在面临不确定性时减少其预测的特异性。本文首先形式化了分层风险和覆盖，并介绍了分层风险覆盖曲线。接下来，我们开发了针对分层选择分类的算法（也称为 “推理规则”），并提出了一种能够以高概率保证目标准确性约束的高效算法。最后，我们对一千多个 ImageNet 分类器进行了大量的实证研究，揭示了诸如 CLIP、ImageNet21k 预训练和知识蒸馏等训练制度提升了分层选择性能。